Determining TTOP model parameter importance and overall performance across northern Canada
Bibliographic record
Abstract
Abstract. Modelling current permafrost distribution and response to a warming climate depends on understanding which factors most strongly control ground temperatures. The Temperature at the Top of Permafrost (TTOP) model provides a simple, widely used framework for estimating permafrost presence and thermal state, yet its sensitivity to key parameters remains poorly quantified across diverse northern environments. This study evaluates the relative influence of TTOP model parameters using ground and air temperature data from 330 sites across northern Canada. A leave – one – out cross-validation approach combined with random forest analysis was used to assess both model sensitivity and variable importance. Results show that TTOP performance is dominated by freezing-season conditions—particularly the freezing n-factor and freezing degree days—while thaw-season parameters exert less control. Sensitivity patterns vary by region, with thawing parameters becoming more influential where the duration of the freezing and thawing seasons is similar. Machine-learning results highlight the additional importance of thermal offset and mean surface temperatures, emphasizing the importance of substrate properties. While the model generally reproduces observed ground temperatures well, parameters derived from landcover classes were not transferable between sites, underscoring the importance of locally calibrated inputs. Overall, this study clarifies how different climatic and environmental factors shape the accuracy of permafrost temperature modelling and provides practical guidance for improving parameterization in regional and global permafrost models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".